About
 

Karthikeyan Shanmugam

Research Staff Member

Who they work with

Categories

Causal Inference

I am currently a Research Staff Member at IBM Research AI, NY. Previously, I was a Herman Goldstine Postdoctoral Fellow in the Math Sciences Division at IBM Research, NY. I obtained my Ph.D. in Electrical and Computer Engineering from UT Austin in summer 2016. My advisor at UT was Alex Dimakis. I obtained my MS degree in Electrical Engineering (2010-2012) from the University of Southern California, B.Tech and M.Tech degrees in Electrical Engineering from IIT Madras in 2010.

My research interests broadly lie in Graph algorithms, Machine learning, Optimization, Coding Theory and Information Theory. In machine learning, my recent focus is on graphical model learning, causal inference interpretability in ML and large graph analytics. I also work on problems relating to information flow, storage and caching over networks.

Top Work

Reverse-engineering causal graphs with soft interventions

Reverse-engineering causal graphs with soft interventions

Causal Inference

Causal inference is expensive. Here’s an algorithm for fixing that.

Causal inference is expensive. Here’s an algorithm for fixing that.

Causal Inference

Publications with the MIT-IBM Watson AI Lab

Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge
AISTATS
 
Active Structure Learning of Causal DAGs via Directed Clique Trees
Active Structure Learning of Causal DAGs via Directed Clique Trees
Causal Inference
 
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Causal Inference
 
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
NeurIPS
 
Sample Efficient Active Learning of Causal Trees
Sample Efficient Active Learning of Causal Trees
NeurIPS
 
Causal inference is expensive. Here’s an algorithm for fixing that.
Causal inference is expensive. Here’s an algorithm for fixing that.
Causal Inference
 
Reverse-engineering causal graphs with soft interventions
Reverse-engineering causal graphs with soft interventions
Causal Inference
 
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
AISTATS
 
Size of Interventional Markov Equivalence Classes in Random DAG Models
Size of Interventional Markov Equivalence Classes in Random DAG Models
AISTATS